Struct leaf::layers::container::sequential::Sequential [−] [src]

pub struct Sequential<B: IBackend + LayerOps<f32>> {
    // some fields omitted
}

Sequential Layer

Methods

`impl<B: IBackend + LayerOps<f32> + 'static> Sequential`

`fn empty() -> Sequential`

Create a empty Sequential container layer.

`fn from_config(backend: Rc, config: &SequentialConfig) -> Sequential`

Create a Sequential layer from a SequentialConfig.

`fn init_layers(&mut self, backend: Rc, in_config: &SequentialConfig)`

Initializes a sequential container.

Sets up the structure of the sequential container. It reads the supplied SequentialConfig, connects the input and output blobs of each layer and determines if the backpropagation has to be executed for each tensor and layer.

Trait Implementations

`impl<B: IBackend + LayerOps<f32> + 'static> ILayer for Sequential`

`fn is_container(&self) -> bool`

`fn inputs_data(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn inputs_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn outputs_data(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn outputs_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn learnable_weights(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn learnable_weights_gradients(&self) -> Option<Vec<ArcLock<SharedTensor<f32>>>>`

`fn learnable_weights_names(&self) -> Option<Vec<String>>`

`fn resize_shared_workspace(&mut self, backend: Rc, workspace: Option<ArcLock<SharedTensor<u8>>>) -> Option<ArcLock<SharedTensor<u8>>>`

`fn forward(&self, backend: &B, input_data: &[ArcLock<SharedTensor<f32>>], weights_data: &[ArcLock<SharedTensor<f32>>], output_data: &mut [ArcLock<SharedTensor<f32>>])`

`fn backward_input(&self, backend: &B, weights_data: &[ArcLock<SharedTensor<f32>>], output_data: &[ArcLock<SharedTensor<f32>>], output_gradients: &[ArcLock<SharedTensor<f32>>], input_data: &[ArcLock<SharedTensor<f32>>], input_gradients: &mut [ArcLock<SharedTensor<f32>>])`

`fn backward_parameters(&self, backend: &B, output_data: &[ArcLock<SharedTensor<f32>>], output_gradients: &[ArcLock<SharedTensor<f32>>], input_data: &[ArcLock<SharedTensor<f32>>], weights_gradients: &mut [ArcLock<SharedTensor<f32>>])`

`fn init(&mut self, backend: Rc)`

`fn reshape(&mut self, backend: Rc, input_data: &mut Vec<ArcLock<SharedTensor<f32>>>, input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>, weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>, weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>, output_data: &mut Vec<ArcLock<SharedTensor<f32>>>, output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>)`

`fn sync(&self, backend: &B, input_data: &mut [ArcLock<SharedTensor<f32>>], input_gradients: &mut [ArcLock<SharedTensor<f32>>], weights_data: &mut [ArcLock<SharedTensor<f32>>], weights_gradients: &mut [ArcLock<SharedTensor<f32>>], output_data: &mut Vec<ArcLock<SharedTensor<f32>>>, output_gradients: &mut Vec<ArcLock<SharedTensor<f32>>>)`

`fn auto_output_blobs(&self) -> bool`

`fn min_output_blobs(&self) -> usize`

`fn exact_num_output_blobs(&self) -> Option<usize>`

`fn auto_weight_blobs(&self) -> bool`

`fn exact_num_input_blobs(&self) -> Option<usize>`

`fn allow_force_backward(&self, input_id: usize) -> bool`

`fn sync_native(&self) -> bool`

`fn compute_in_place(&self) -> bool`

`fn loss_weight(&self, output_id: usize) -> Option<f32>`

`fn learnable_weights_lr(&self) -> Option<Vec<Option<f32>>>`

`impl<B: IBackend + LayerOps<f32> + 'static> ComputeOutput<f32, B> for Sequential`

`fn compute_output(&self, backend: &B, weights: &[&SharedTensor<f32>], input_data: &[&SharedTensor<f32>], output_data: &mut [&mut SharedTensor<f32>])`

`impl<B: IBackend + LayerOps<f32> + 'static> ComputeInputGradient<f32, B> for Sequential`

`fn compute_input_gradient(&self, backend: &B, weights_data: &[&SharedTensor<f32>], output_data: &[&SharedTensor<f32>], output_gradients: &[&SharedTensor<f32>], input_data: &[&SharedTensor<f32>], input_gradients: &mut [&mut SharedTensor<f32>])`

`impl<B: IBackend + LayerOps<f32> + 'static> ComputeParametersGradient<f32, B> for Sequential`

`fn compute_parameters_gradient(&self, backend: &B, output_data: &[&SharedTensor<f32>], output_gradients: &[&SharedTensor<f32>], input_data: &[&SharedTensor<f32>], parameters_gradients: &mut [&mut SharedTensor<f32>])`

Derived Implementations

`impl<B: Debug + IBackend + LayerOps<f32>> Debug for Sequential`

`fn fmt(&self, __arg_0: &mut Formatter) -> Result`